本文主要介绍了Transfer Learning的概念和分类，以及一些相关的TL算法。主要内容：
（1）Source Domain and Target Domain (TL focus on Target Domain) （特征空间，边缘概率分布，学习目标）
（2）相关的领域：learning to learn; life-long; knowledge transfer; inductive transfer; multitask learning; knowledge consolidation; context-sensitive learning; knowledge-based inductive bias; metalearning; incremental/cumulatie learning;
（3）迁移学习分类：a) Inductive TL; b) Unsupervised TL; c) Transductive TL
（4）TL方法分类：
（5）negative transfer （计算source domain和target domain之间的距离 ）
（6）几个思考方向： a)迁移什么；b)怎么迁移；c)什么时候迁移
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| A Survey on Transfer Learning | |||||||||||
| 文献作者 | Sinno Jialin Pan and Qiang Yang | ||||||||||
| 文献发表年限 | 2010 | ||||||||||
| 文献关键字 | |||||||||||
| Transfer learning, survey, machine learning, data mining. | |||||||||||
| 摘要描述 | |||||||||||
| A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research. | |||||||||||
